Multicriteria Classifier Ensemble Learning for Imbalanced Data
One of the vital problems with the imbalanced data classifier training is the definition of an optimization criterion. Typically, since the exact cost of misclassification of the individual classes is unknown, combined metrics and loss functions that roughly balance the cost for each class are used....
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9706443/ |
| _version_ | 1811309331469041664 |
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| author | Weronika Wegier Michal Koziarski Micha Wozniak |
| author_facet | Weronika Wegier Michal Koziarski Micha Wozniak |
| author_sort | Weronika Wegier |
| collection | DOAJ |
| description | One of the vital problems with the imbalanced data classifier training is the definition of an optimization criterion. Typically, since the exact cost of misclassification of the individual classes is unknown, combined metrics and loss functions that roughly balance the cost for each class are used. However, this approach can lead to a loss of information, since different trade-offs between class misclassification rates can produce similar combined metric values. To address this issue, this paper discusses a multi-criteria ensemble training method for the imbalanced data. The proposed method jointly optimizes <italic>precision</italic> and <italic>recall</italic>, and provides the end-user with a set of Pareto optimal solutions, from which the final one can be chosen according to the user’s preference. The proposed approach was evaluated on a number of benchmark datasets and compared with the single-criterion approach (where the selected criterion was one of the chosen metrics). The results of the experiments confirmed the usefulness of the obtained method, which on the one hand guarantees good quality, i.e., not worse than the one obtained with the use of single-criterion optimization, and on the other hand, offers the user the opportunity to choose the solution that best meets their expectations regarding the trade-off between errors on the minority and the majority class. |
| first_indexed | 2024-04-13T09:40:27Z |
| format | Article |
| id | doaj.art-dc2d1fbec9084d38a44e5e095cd1bbe7 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-13T09:40:27Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-dc2d1fbec9084d38a44e5e095cd1bbe72022-12-22T02:51:56ZengIEEEIEEE Access2169-35362022-01-0110168071681810.1109/ACCESS.2022.31499149706443Multicriteria Classifier Ensemble Learning for Imbalanced DataWeronika Wegier0https://orcid.org/0000-0002-9339-2669Michal Koziarski1Micha Wozniak2https://orcid.org/0000-0003-0146-4205Department of Systems and Computer Networks, Wrocław University of Science and Technology, Wrocław, PolandDepartment of Electronics, AGH University of Science and Technology, Kraków, PolandDepartment of Systems and Computer Networks, Wrocław University of Science and Technology, Wrocław, PolandOne of the vital problems with the imbalanced data classifier training is the definition of an optimization criterion. Typically, since the exact cost of misclassification of the individual classes is unknown, combined metrics and loss functions that roughly balance the cost for each class are used. However, this approach can lead to a loss of information, since different trade-offs between class misclassification rates can produce similar combined metric values. To address this issue, this paper discusses a multi-criteria ensemble training method for the imbalanced data. The proposed method jointly optimizes <italic>precision</italic> and <italic>recall</italic>, and provides the end-user with a set of Pareto optimal solutions, from which the final one can be chosen according to the user’s preference. The proposed approach was evaluated on a number of benchmark datasets and compared with the single-criterion approach (where the selected criterion was one of the chosen metrics). The results of the experiments confirmed the usefulness of the obtained method, which on the one hand guarantees good quality, i.e., not worse than the one obtained with the use of single-criterion optimization, and on the other hand, offers the user the opportunity to choose the solution that best meets their expectations regarding the trade-off between errors on the minority and the majority class.https://ieeexplore.ieee.org/document/9706443/Classifier ensembleimbalanced datamulti-objective optimizationpattern classification |
| spellingShingle | Weronika Wegier Michal Koziarski Micha Wozniak Multicriteria Classifier Ensemble Learning for Imbalanced Data IEEE Access Classifier ensemble imbalanced data multi-objective optimization pattern classification |
| title | Multicriteria Classifier Ensemble Learning for Imbalanced Data |
| title_full | Multicriteria Classifier Ensemble Learning for Imbalanced Data |
| title_fullStr | Multicriteria Classifier Ensemble Learning for Imbalanced Data |
| title_full_unstemmed | Multicriteria Classifier Ensemble Learning for Imbalanced Data |
| title_short | Multicriteria Classifier Ensemble Learning for Imbalanced Data |
| title_sort | multicriteria classifier ensemble learning for imbalanced data |
| topic | Classifier ensemble imbalanced data multi-objective optimization pattern classification |
| url | https://ieeexplore.ieee.org/document/9706443/ |
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